In this paper, we propose a novel end-to-end framework called SegCrowdNet. To the best of our knowledge, the proposed SegCrowdNet is the first network to utilize the segmentation attention mechanism in crowd counting. It adaptively highlights the human head region and suppresses the non-head region by way of optimizing a novel loss. With the guidance of the segmentation attention mechanism, the proposed SegCrowdNet pays more attention to the human head region and automatically encodes the highly refined density map. Our proposed SegCrowdNet can also automatically adapt the variation of crowd counts by learning a classification function. Extensive experiments are conducted on ShanghaiTech Part_A dataset [1]}, ShanghaiTech Part_B dataset [1]}, UCF_CC_50 dataset [3]} and WorldExpo’10 dataset [4]}. The results demonstrate that our proposed method outperforms many state-of-the-art methods.
<FIGURE><FIGURE>